A machine-learning model for prediction of Acinetobacter baumannii hospital acquired infection.

Journal: PloS one
PMID: 39636870

Abstract

BACKGROUND: Acinetobacter baumanni infection is a leading cause of morbidity and mortality in the Intensive Care Unit (ICU). Early recognition of patients at risk for infection allows early proper treatment and is associated with improved outcomes. This study aimed to construct an innovative Machine Learning (ML) based prediction tool for Acinetobacter baumanni infection, among patients in the ICU, and to examine its robustness and predictive power.

Authors

  • Ido Neuman
    Department of General Intensive Care and Institute for Nutrition Research, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel.
  • Leonid Shvartser
    TSG IT Advanced Systems Ltd., Or Yehuda, Israel.
  • Shmuel Teppler
    TSG IT Advanced Systems Ltd., Or Yehuda, Israel.
  • Yehoshua Friedman
    Housetable Ltd., Jerusalem, Israel.
  • Jacob J Levine
    Harel Insurance, Jerusalem, Israel.
  • Ilya Kagan
    Department of General Intensive Care and Institute for Nutrition Research, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel.
  • Jihad Bishara
    Infectious Diseases Unit, Rabin Medical Center, Beilinson Hospital, Petah-Tikva, Israel.
  • Shiri Kushinir
    Rabin Medical Center Research Authority, Beilinson Hospital, Petah Tikva, Israel.
  • Pierre Singer
    Department of General Intensive Care and Institute for Nutrition Research, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel.

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